Detection and Discrimination of Tea Plant Stresses Based on Hyperspectral Imaging Technique at a Canopy Level

Cui, Lihan; Yan, Lijie; Xin, Zhao; Yuan, Lin; Jin, Jing; Zhang, Jingcheng

doi:10.32604/phyton.2021.015511

Cited by 6 publications

(6 citation statements)

References 23 publications

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“…The number of samples with individual accuracies below 0.60 accounted for approximately 18% of external test samples, those between 0.6 and 0.85 were 17%, and those above 0.85 were 64%. It should be noted that the metal and glass samples can cause the halation of excessive reflectance intensities that might be generated from the specular reflection of samples, causing overexposure in some pixels 49 with high spectral reflectance (bright spots in Figure S6). Therefore, the spectra of the inert group in the external test set (Figure S9B) showed larger fluctuations than those in the training and test sets (Figure S9A).…”

Section: ■ Results and Discussionmentioning

confidence: 99%

Machine Learning and Hyperspectral Imaging-Aided Forecast for the Share of Biogenic and Fossil Carbon in Solid Waste

Lan

et al. 2023

ACS Sustainable Chem. Eng.

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Traditional methods for analyzing the biogenic and fossil carbon shares in solid waste are time-consuming and labor-intensive. A novel approach was developed to directly classify the carbon group and predict carbon content using the hyperspectral imaging (HSI) spectra of solid waste in conjunction with state-of-the-art tree-based machine learning models, including random forest (RF), extreme gradient boost, and light gradient boost machine (LGBM). All of the classifiers and regressors were able to achieve an accuracy above 0.95 and an R 2 of 0.96 in the test set, respectively. In addition, two model interpretation approaches, the Shapley additive explanation and model explainer, were applied. The results showed that the predictions of the developed models were based on a reasonable understanding of the overtone and shake of the functional groups (C–H, N–H, and O–H). Furthermore, the developed models were validated by an external test set, which did not overlap with the data used for model construction. The RF and LGBM showed robust performance with a 0.790 accuracy for carbon group classification and a 0.806 R 2 for carbon content prediction. Overall, the optimal models provided a rapid method for characterizing the biogenic carbon share in solid waste based on raw HSI spectra without preprocessing.

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Section: ■ Results and Discussionmentioning

confidence: 99%

Machine Learning and Hyperspectral Imaging-Aided Forecast for the Share of Biogenic and Fossil Carbon in Solid Waste

Lan

et al. 2023

ACS Sustainable Chem. Eng.

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“…However, compared to RGB imaging, the acquisition of multispectral and hyperspectral imaging can be more complex and slower. Additionally, hyperspectral imaging is more susceptible to the effects of illumination and environmental factors, and greatly relies on accurate data-analysis techniques and reliable sensing systems [71,73]. However, ongoing technological advancements would make multispectral and hyperspectral imaging more high-performing and lightweight [74,75].…”

Section: Multispectral and Hyperspectral Imagingmentioning

confidence: 99%

A Synthetic Review of Various Dimensions of Non-Destructive Plant Stress Phenotyping

et al. 2023

Plants

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Non-destructive plant stress phenotyping begins with traditional one-dimensional (1D) spectroscopy, followed by two-dimensional (2D) imaging, three-dimensional (3D) or even temporal-three-dimensional (T-3D), spectral-three-dimensional (S-3D), and temporal-spectral-three-dimensional (TS-3D) phenotyping, all of which are aimed at observing subtle changes in plants under stress. However, a comprehensive review that covers all these dimensional types of phenotyping, ordered in a spatial arrangement from 1D to 3D, as well as temporal and spectral dimensions, is lacking. In this review, we look back to the development of data-acquiring techniques for various dimensions of plant stress phenotyping (1D spectroscopy, 2D imaging, 3D phenotyping), as well as their corresponding data-analyzing pipelines (mathematical analysis, machine learning, or deep learning), and look forward to the trends and challenges of high-performance multi-dimension (integrated spatial, temporal, and spectral) phenotyping demands. We hope this article can serve as a reference for implementing various dimensions of non-destructive plant stress phenotyping.

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“…Therefore, hyperspectral imaging is an effective means for the phenotypic analysis of plant diseases and pests. Cui et al 15 used Fisher discriminant analysis established models to detect damaged areas and distinguish three kinds of stresses, with accuracies of 95% and 98%, respectively. Chen et al 54 set up a hyperspectral system to detect the drought stress degree of tea seedlings.…”

Section: Application Of Hyperspectral Imaging Technology In Teamentioning

confidence: 99%

“…Studies have shown that hyperspectral technology can observe and detect plant diseases and pests. 15 At present, the use of hyperspectral technology to detect the quality of agricultural products is still in its infancy. However, as a fast and nondestructive testing technology, hyperspectral imaging has great application prospects.…”

Section: Introductionmentioning

confidence: 99%

“…As a nondestructive testing technology, HSI could respond to changes in plant morphology, material composition, and physiological and biochemical status, so it has great potential in detecting tea garden diseases, pests, and other stresses. Studies have shown that hyperspectral technology can observe and detect plant diseases and pests 15 . At present, the use of hyperspectral technology to detect the quality of agricultural products is still in its infancy.…”

Section: Introductionmentioning

confidence: 99%

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Research progress on the application of hyperspectral imaging techniques in tea science

Liang

Zhou

Ling

et al. 2023

Journal of Chemometrics

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Hyperspectral imaging technology combines two‐dimensional imaging and spectral technology, which can simultaneously obtain spatial and spectral information of the object to be measured and is an advanced technical method. With the development of science and technology, the detection of tea has also been continuously improved, and it has developed in the direction of being nondestructive, fast, real‐time, and accurate. In this paper, the principle of hyperspectral imaging technology is introduced, and according to research on hyperspectral imaging technology in the nondestructive rapid detection of tea in the past 5 years, the application of hyperspectral imaging technology in the detection of tea biochemical components, accurate classification, determination of mildew degree, and stress monitoring and the application progress in planting production management are analyzed. Additionally, the main challenges existing in the current research are analyzed, and future application prospects are proposed to provide a reference for the application and promotion of hyperspectral imaging technology in the actual production of tea.

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Detection and Discrimination of Tea Plant Stresses Based on Hyperspectral Imaging Technique at a Canopy Level

Cited by 6 publications

References 23 publications

Machine Learning and Hyperspectral Imaging-Aided Forecast for the Share of Biogenic and Fossil Carbon in Solid Waste

Machine Learning and Hyperspectral Imaging-Aided Forecast for the Share of Biogenic and Fossil Carbon in Solid Waste

A Synthetic Review of Various Dimensions of Non-Destructive Plant Stress Phenotyping

Research progress on the application of hyperspectral imaging techniques in tea science

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